CN111681607B - Gamma adjusting method and system based on genetic algorithm - Google Patents
Gamma adjusting method and system based on genetic algorithm Download PDFInfo
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Abstract
The invention discloses a Gamma adjustment method and a system based on a genetic algorithm, which are characterized in that a primary generation population is obtained, and individuals in the primary generation population are a set of associated binding points corresponding to each adjustment binding point; obtaining the fitness of each individual in the population, namely selecting an adjusted module sample, obtaining a Gamma adjustment predicted value of each adjustment binding point by using the adjustment data of the associated binding point, and obtaining the fitness of each individual by using the Gamma adjustment convergence value and the Gamma adjustment predicted value of each adjustment binding point; updating the population by using the fitness of each individual, repeating the population updating process until the maximum iteration times are reached, and taking the individual with the maximum fitness in the population when the iteration is terminated as the optimal individual; and determining the associated binding point of each binding point of the module to be modulated by the optimal individual to realize the corresponding Gamma adjustment, so as to improve the accuracy of the Gamma initial value prediction and adjust the convergence speed.
Description
Technical Field
The invention belongs to the field of Gamma adjustment, and particularly relates to a Gamma adjustment method and system based on a genetic algorithm.
Background
On the OLED production line, Gamma adjustment is an iterative optimization technique for adjusting the panel brightness and chromaticity by changing the module register value (or voltage value). The aim is to coordinate the real linear response of the module with the nonlinear response under the perception of human eyes, so as to achieve the luminous effect of natural transition and distinct hierarchy. Gamma adjustment speed is generally optimized from two aspects of initial value prediction and iterative search process, and under the premise that the current traditional initial value prediction method and iterative optimization method are basically stable, the method has important significance for searching the topological structure between Gamma adjustment binding points through a large amount of data so as to optimize the initial value prediction and iterative search process.
Due to the fine noise of the OLED module in the process and the difference of the molecular arrangement on the same coating, the responses of different modules to the electric signals on the same production line are different, which is the main difficulty of Gamma adjustment. Moreover, the response of the same module is not stable (time-varying), which makes high-precision Gamma adjustment difficult.
The initial value prediction algorithm used at present is mostly based on the adjusted convergence data of the same binding point of the module for prediction, and the iterative optimization algorithm is mostly based on the change rule of the brightness and the chromaticity of the module or the adjusted convergence data of the same binding point of the module for adjustment. These algorithms basically use only similar laws between the same binding points of different modules, which is feasible when the difference in the photoelectric characteristics between the modules is not large, but both the prediction effect and the adjustment effect are affected when the difference is large.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a Gamma adjustment method and a Gamma adjustment system based on a genetic algorithm, which automatically search a Gamma binding point topological structure according to Gamma adjustment convergence data, and use the topological structure for Gamma initial value prediction and Gamma iterative adjustment so as to improve the accuracy of Gamma initial value prediction and adjust convergence speed and effectively shorten the time required by Gamma adjustment.
To achieve the above object, according to one aspect of the present invention, there is provided a Gamma adjustment method based on a genetic algorithm, the method including:
acquiring a primary generation population, wherein individuals in the primary generation population are a set of associated binding points corresponding to each adjusting binding point;
obtaining the fitness of each individual in the population, namely selecting an adjusted module sample, obtaining a Gamma adjustment predicted value of each adjustment binding point by using the adjustment data of the associated binding point, and obtaining the fitness of each individual by using the Gamma adjustment convergence value and the Gamma adjustment predicted value of each adjustment binding point;
updating the population by using the fitness of each individual, repeating the population updating process until the maximum iteration times are reached, and taking the individual with the maximum fitness in the population when the iteration is terminated as the optimal individual; the associated binding point of each binding point of the module to be modulated is determined in an optimal individual to realize the corresponding Gamma adjustment.
As a further improvement of the present invention, the object of Gamma adjustment is a binding register value or a voltage value.
As a further improvement of the present invention, when the number of the module samples is plural, the fitness of the individual is the sum of the fitness corresponding to each module sample.
As a further improvement of the present invention, updating the population with the fitness of each individual comprises:
and selecting the population by utilizing the fitness of each individual, and performing cross operation or mutation operation on a plurality of selected individuals to form a next generation population.
As a further improvement of the present invention, the selecting of the population using the fitness of each individual comprises:
the individual with the highest fitness is reserved, and the remaining individuals are selected through a roulette selection method.
As a further improvement of the invention, the individuals in the population are selected according to a certain probability to carry out the cross operation, and the cross operation is to exchange a part of the codes of the two individuals so as to form a new individual.
As a further improvement of the invention, individuals in the population are selected according to a certain probability to carry out mutation operation, and the mutation operation is to carry out random mutation on values of certain positions of the individuals, thereby forming new individuals.
As a further improvement of the invention, the sum of Euclidean distances between the Gamma adjustment convergence value and the Gamma adjustment predicted value of each binding point is calculated, and the reciprocal of the sum is taken as the fitness of the individual.
To achieve the above object, according to another aspect of the present invention, there is provided a Gamma adjustment system based on a genetic algorithm, the system comprising:
the initial generation population acquisition module is used for acquiring individuals in the initial generation population, namely a set of associated binding points corresponding to each adjusting binding point;
the individual fitness obtaining module is used for selecting the adjusted module samples, obtaining the Gamma adjustment predicted value of each adjustment binding point by using the adjustment data of the associated binding point, and obtaining the fitness of each individual by using the Gamma adjustment convergence value and the Gamma adjustment predicted value of each adjustment binding point;
the population updating module is used for updating the population by using the fitness of each individual, repeating the population updating process until the maximum iteration times is reached, and taking the individual with the maximum fitness in the population when the iteration is terminated as the optimal individual;
and the Gamma adjustment module is used for determining the associated binding point of each binding point of the module to be modulated by the optimal individual so as to realize the corresponding Gamma adjustment.
To achieve the above object, according to another aspect of the present invention, there is provided a computer-readable medium storing a computer program executable by a terminal device, the program, when executed on the terminal device, causing the terminal device to perform the steps of the above method.
Generally, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:
the invention discloses a Gamma adjustment method and system based on genetic algorithm, which are based on genetic algorithm, and search out the topological structure between the binding points of the same module by observing the similarity of the adjacent binding points in the spatial distribution in the Gamma adjustment process, thereby greatly reducing the dependence of the adjustment algorithm on the module consistency, achieving the purpose of improving the Gamma adjustment speed, and efficiently finding out the topological structure between the binding points in a short time, thereby providing possibility for dynamically adjusting an initial value prediction algorithm or an iterative optimization algorithm based on the adjusted data of the current module to improve the Gamma adjustment efficiency, providing basis for reducing the band of the module and reducing the binding points, and effectively improving the accuracy of initial value prediction by the searched topological structure from the simulation effect.
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FIG. 1 is a schematic diagram of a Gamma adjustment method based on genetic algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a topology characterized by an individual of an embodiment of the present invention;
FIG. 3 is a schematic diagram of individual crossbar operation according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other. The present invention will be described in further detail with reference to specific embodiments.
Fig. 1 is a schematic diagram of a Gamma adjustment method based on a genetic algorithm according to an embodiment of the present invention. As shown in fig. 1, a Gamma adjustment method based on a genetic algorithm includes:
a set formed by one associated binding point identifier corresponding to each adjusting binding point is used as an individual, a plurality of individuals can be generated as an initial generation population according to various possible association modes, and the individuals in the initial generation population are used for representing the corresponding relation between the adjusting binding point and the associated binding point; for the module for Gamma adjustment, the adjustment sequence of each binding point in the Gamma adjustment process is fixed, but a certain binding point can be used as a corresponding associated binding point, and the corresponding relation is applied to Gamma initial value prediction and Gamma iterative adjustment, so that the accuracy of Gamma initial value prediction and the adjustment convergence speed are improved, and the time required by Gamma adjustment is effectively shortened.
FIG. 2 is a schematic diagram of a topology characterized by an individual of an embodiment of the present invention. As shown in fig. 2, as an example, the numbers of the bindings to be adjusted of the module to be adjusted are numbers 1, 2, …, and 18 in sequence, the first adjusted binding is taken as a root binding, and an associated binding (parent binding) of each subsequent binding is generated, so as to form a tree structure, where the associated binding is a node that has undergone Gamma adjustment before the modulation binding; for example, the number 2 binding can only use the number 1 binding as its associated binding, and the associated binding of the number 3 binding can be the number 1 binding or the number 2 binding, so that a certain number of initial structures, i.e., associated binding identifier sets, can be randomly generated. Specifically, the common encoding modes of the genetic algorithm include binary encoding, floating point number encoding and the like, as an example, each binding point has a unique associated node, the whole tree structure can be described through the associated nodes, the associated node numbers can be stored in an array, and the whole array represents a whole encoded object.
The adjustment predicted value can also be used as a Gamma adjustment initial value, and the Gamma adjustment predicted value of each binding point can be obtained by using an initial value prediction algorithm, wherein the object of Gamma adjustment can be a register value corresponding to the binding point, can also be a voltage value, and of course, other adjustment objects can also be selected according to the requirement of Gamma adjustment; and the more suitable the selected associated binding points are, the fewer the Gamma adjustment steps corresponding to the associated binding points are, so that individuals can be constructed by utilizing the associated binding points, and the genetic algorithm is utilized to carry out screening and genetic variation on the individuals so as to obtain the associated binding points corresponding to each binding point, thereby improving the Gamma adjustment efficiency.
The population updating process comprises the steps of obtaining the fitness of each individual under current iteration, wherein a Gamma adjusted module sample is selected, a Gamma adjustment predicted value of each binding point is obtained by utilizing the adjustment data associated with the binding point, and the fitness of each individual is obtained by utilizing the Gamma adjustment convergence value and the Gamma adjustment predicted value of each binding point; and updating the population in the genetic algorithm by using the fitness of each individual. In a preferred embodiment, when the number of the pattern samples is plural, the fitness of a certain individual is the sum of the fitness corresponding to each pattern sample.
Fitness is an index used for measuring the quality of individuals in a population. Fitness in genetic algorithms is the value of the criterion of the combination of features. The genetic algorithm does not generally need other external information in the search evolution process, and only uses an evaluation function to evaluate the quality of an individual or a solution and is used as a basis for subsequent genetic operation. The method for forming the fitness by utilizing the difference degree between the Gamma adjustment convergence value and the Gamma adjustment predicted value of the binding point can be diversified, common Euclidean distance, variance formulas and the like can be used for obtaining the fitness, as a preferable scheme, the sum of the Euclidean distances between the Gamma adjustment convergence value and the Gamma adjustment predicted value of each binding point can be calculated, and the reciprocal of the sum is used as the individual fitness.
For genetic algorithms, there are various implementation manners for implementing population update by using the fitness of each individual, and generally, the population can be selected by using the fitness of each individual, and the selected individuals are subjected to cross and variation operations to form a next generation population; specifically, the selection operation in the genetic algorithm is used to determine how to select some individuals from the parent population according to a certain method for inheritance to the next generation population, generally, the probability that the individuals with higher fitness are selected is higher, and the commonly used selection probability methods corresponding to fitness are roulette selection, random competition selection and optimal reservation selection.
The crossover operation of genetic algorithms means that two individuals randomly selected are exchanged for a portion of their codes, thereby forming a new individual. Common crossing modes include single-point crossing, multi-point crossing, uniform crossing and the like, and the corresponding crossing mode can be selected by combining with actual requirements. FIG. 3 is a schematic diagram of individual crossbar operation according to an embodiment of the present invention. As shown in fig. 3, as an example, the numbers of the binding points to be adjusted of the module to be adjusted are numbers 1, 2, …, and 18 in sequence, as a preferred mode, a single-point crossing mode may be adopted, that is, a point is randomly selected from an encoded array, and two randomly selected individuals exchange array contents from the point backward, so that a crossing is completed, that is, an upper left individual and an upper right individual in the figure are crossed from the node number 9 to obtain a lower left individual and a lower right individual, and a crossing process is completed to obtain two new individuals.
The mutation operation of the genetic algorithm refers to randomly mutating values of certain positions of individuals to form new individuals, and optionally, a single-point mutation mode is adopted, namely, one binding point in the individuals is randomly selected, and one binding point is found to be used as an associated binding point in the vicinity of the binding point again, so that a mutation process is completed.
Repeating the population updating process until the maximum iteration times are reached, and taking the individual with the maximum fitness in the population when the iteration is terminated as the optimal individual; the associated binding point of each binding point of the module to be modulated is determined in an optimal individual to realize the corresponding Gamma adjustment.
Comparing the effect of gamma adjustment of the common topological structure with the effect of gamma adjustment of the topological structure obtained by searching by the method of the embodiment of the invention, wherein 50 initialization population individuals are selected as the searching parameters, iteration is carried out for 2000 times, ten module data are selected for prediction, the average deviation of register values is calculated respectively, and the simulation result shows that the topological structure obtained by searching by the method of the embodiment of the invention can stably and effectively improve the accuracy of an initial value prediction algorithm, and the adjustment efficiency of a test module is improved by about 25%.
A genetic algorithm based Gamma modulation system, the system comprising:
the initial generation population acquisition module is used for acquiring individuals in the initial generation population, namely a set of associated binding points corresponding to each adjusting binding point;
the individual fitness obtaining module is used for selecting the adjusted module samples, obtaining the Gamma adjustment predicted value of each adjustment binding point by using the adjustment data of the associated binding point, and obtaining the fitness of each individual by using the Gamma adjustment convergence value and the Gamma adjustment predicted value of each adjustment binding point;
the population updating module is used for updating the population by using the fitness of each individual, repeating the population updating process until the maximum iteration times is reached, and taking the individual with the maximum fitness in the population when the iteration is terminated as the optimal individual;
and the Gamma adjustment module is used for determining the associated binding point of each binding point of the module to be modulated by the optimal individual so as to realize the corresponding Gamma adjustment. The implementation principle and technical effect of the system are similar to those of the method, and are not described herein again.
A computer-readable medium, in which a computer program executable by a terminal device is stored, causes the terminal device to perform the steps of the above-mentioned method when the program is run on the terminal device.
A terminal device comprising at least one processing unit and at least one memory unit, wherein the memory unit stores a computer program which, when executed by the processing unit, causes the processing unit to carry out the steps of the above-mentioned method.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A Gamma adjustment method based on genetic algorithm is characterized by comprising the following steps:
acquiring a primary generation population, wherein individuals in the primary generation population are associated binding point sets corresponding to each adjusting binding point;
obtaining the fitness of each individual in the population, namely selecting an adjusted module sample, obtaining a Gamma adjustment predicted value of each adjustment binding point by using the adjustment data of the associated binding point, and obtaining the fitness of each individual by using the Gamma adjustment convergence value and the Gamma adjustment predicted value of each adjustment binding point;
updating the population by using the fitness of each individual, repeating the population updating process until the maximum iteration times are reached, and taking the individual with the maximum fitness in the population when the iteration is terminated as the optimal individual; and determining the associated binding point of each binding point of the module to be modulated by the optimal individual so as to realize the corresponding Gamma adjustment.
2. The Gamma adjustment method based on genetic algorithm as claimed in claim 1, wherein the object of Gamma adjustment is a binding register value or a voltage value.
3. The Gamma adjustment method based on genetic algorithm of claim 1, wherein when the number of the model samples is plural, the fitness of the individual is the sum of the fitness corresponding to each model sample.
4. The method of any one of claims 1-3, wherein updating the population with the fitness of each individual comprises:
and selecting the population by utilizing the fitness of each individual, and performing cross operation or mutation operation on a plurality of selected individuals to form a next generation population.
5. The method for Gamma adjustment based on genetic algorithm as claimed in claim 4, wherein the selecting the population with the fitness of each individual comprises:
the individual with the highest fitness is reserved, and the remaining individuals are selected through a roulette selection method.
6. The Gamma adjustment method based on genetic algorithm as claimed in claim 4, wherein the individuals in the population are selected according to a certain probability to perform the crossover operation, and the crossover operation is to exchange a part of the two individual codes to form a new individual.
7. The Gamma adjustment method based on genetic algorithm as claimed in claim 4, wherein the individuals in the population are selected according to a certain probability to perform mutation operation, and the mutation operation is to randomly mutate the values of certain positions of the individuals to form new individuals.
8. The Gamma adjustment method based on genetic algorithm according to any one of claims 1 to 3, wherein the sum of Euclidean distances between the convergence value of Gamma adjustment and the predicted value of Gamma adjustment of each binding point is calculated, and the reciprocal of the sum is taken as the fitness of the individual.
9. A system for Gamma modulation based on genetic algorithms, the system comprising:
the initial generation population acquisition module is used for acquiring individuals in the initial generation population, namely an associated binding point set corresponding to each regulation binding point;
the individual fitness obtaining module is used for selecting the adjusted module samples, obtaining the Gamma adjustment predicted value of each adjustment binding point by using the adjustment data of the associated binding point, and obtaining the fitness of each individual by using the Gamma adjustment convergence value and the Gamma adjustment predicted value of each adjustment binding point;
the population updating module is used for updating the population by using the fitness of each individual, repeating the population updating process until the maximum iteration times is reached, and taking the individual with the maximum fitness in the population when the iteration is terminated as the optimal individual;
and the Gamma adjustment module is used for determining the associated binding point of each binding point of the module to be modulated by the optimal individual so as to realize the corresponding Gamma adjustment.
10. A computer-readable medium, in which a computer program is stored which is executable by a terminal device, and which, when run on the terminal device, causes the terminal device to carry out the steps of the method of any one of claims 1 to 8.
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